Engineers and analysts querying a database may not know each other. As a result, they may end up duplicating work in generating semantically accurate queries of a database. These queries may be generated at different time periods, in different locations, and/or in different semantic terms. The problem may become more pronounced in an organization (e.g., General Electric®, Amazon®, Safeway®, etc.) having numerous (e.g., dozens, hundreds, thousands) of individuals querying the same database. Over time, different engineers and/or analysts may use their own terms when defining elements, variables, and/or attributes of the database. This may add to the complexity because different words for the same underlying meaning of content and structure of the database.
As a result, each engineer and/or analyst may have to relearn how the database is organized from scratch, with no guidance and/or input from those that may have advanced knowledge through previous interaction with the database with similar queries. As a result, the engineer and/or analyst may spend a substantial amount of time in self learning a detailed understanding of the database schema, design, and/or table structure prior to generating a query by manually observing query logs and database structures. Even when the engineer and/or analysts understands the database, they may waste a significant amount of time in experimentation in generating semantically accurate queries to the database in seeking an answer sought by the organization. This may be expensive and wasteful for the organization.